Machine learning-based closed-loop for optimizing HOMO–LUMO gap in diarylethene

Key Points

• The aim is to use machine learning to optimize the HOMO-LUMO gap in diarylethene compounds for better photoresponsive materials.
• Utilized a machine learning-driven strategy
• Focused on diarylethene switches
• Optimized electronic gaps
• Achieved precise tuning of electronic gaps
• Accelerated the design process for high-performance materials